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Autism Spectrum Disorder

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Dynamic multi-hypergraph structure learning for disease diagnosis on multimodal data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
With its superior capability in complex data modeling, hypergraph computation is a powerful tool for many applications. In this work, we propose using hypergraph computation for disease prediction. Hypergraphs allow for the representation of higher-o...

Capsule DenseNet++: Enhanced autism detection framework with deep learning and reinforcement learning-based lifestyle recommendation.

Computers in biology and medicine
Autism Spectrum Disorder (ASD) is a complex neurological condition that impairs the ability to interact, communicate, and behave. It is becoming increasingly prevalent worldwide, with an increase in the number of young children diagnosed with ASD in ...

Optimized attention-enhanced U-Net for autism detection and region localization in MRI.

Psychiatry research. Neuroimaging
Autism spectrum disorder (ASD) is a neurodevelopmental condition that affects a child's cognitive and social skills, often diagnosed only after symptoms appear around age 2. Leveraging MRI for early ASD detection can improve intervention outcomes. Th...

Self-training EEG discrimination model with weakly supervised sample construction: An age-based perspective on ASD evaluation.

Neural networks : the official journal of the International Neural Network Society
Deep learning for Electroencephalography (EEG) has become dominant in the tasks of discrimination and evaluation of brain disorders. However, despite its significant successes, this approach has long been facing challenges due to the limited availabi...

Semi-Automated Multi-Label Classification of Autistic Mannerisms by Machine Learning on Post Hoc Skeletal Tracking.

Autism research : official journal of the International Society for Autism Research
Mannerisms describe repetitive or unconventional body movements like arm flapping. These movements are early markers of restricted and repetitive behaviors (RRBs) in autism spectrum disorder (ASD). However, assessing mannerisms reliably is challengin...

Initial study of verbal and nonverbal communication training through the collaborative operation of a humanoid robot for individuals with autism spectrum disorder.

Asian journal of psychiatry
Individuals with autism spectrum disorder (ASD) experience difficulties in both verbal and nonverbal communication. Collaborative work allows them to use and develop their nonverbal communication abilities. Therefore, we developed a collaborative wor...

MCDGLN: Masked connection-based dynamic graph learning network for autism spectrum disorder.

Brain research bulletin
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by complex physiological processes. Previous research has predominantly focused on static cerebral interactions, often neglecting the brain's dynamic nature and the challen...

Female autism categorization using CNN based NeuroNet57 and ant colony optimization.

Computers in biology and medicine
Autism identification and classification using biomedical medical image analysis has advanced recently. Research shows autistic females have different phenotypic and age-related brain variations than males. Gender-specific hormones and genes affect a...

A Framework for Comparison and Interpretation of Machine Learning Classifiers to Predict Autism on the ABIDE Dataset.

Human brain mapping
Autism is a neurodevelopmental condition affecting ~1% of the population. Recently, machine learning models have been trained to classify participants with autism using their neuroimaging features, though the performance of these models varies in the...

ASiDentify (ASiD): a machine learning model to predict new autism spectrum disorder risk genes.

Genetics
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects nearly 3% of children and has a strong genetic component. While hundreds of ASD risk genes have been identified through sequencing studies, the genetic heterogeneity of ASD ...